Transfer learning seeks to develop methods for training of a model in one environment, then fine-tuning the feature space and decision boundaries to suit implementation in another environment [1]}.
In situations with sparse data optimization limits, transfer learning can utilize domains with rich data, such as lab datasets, to infer necessary knowledge [2]}.
The research on transfer learning in fault diagnosis applications has increased rapidly over the last few years, with many successful transfers between different lab datasets [3]}.
As models improve, transfer learning can enable broader implementation of these models in process industry with a lower demand for labeled instances compared to supervised learning [4]}.
Tasks solvable through transfer learning are the same as in supervised learning, with the added benefit of generalising from one environment to another, thereby reducing the number of label instances needed in the target domain.
Methods used in transfer learning vary;
many publications use transferrable convolutional neural networks [4]}, [6]}, [7]}, [8]}, [9]}, [10]}, [11]}, [12]}, [13]}, [14]}, [15]}, occasionally employed with adversarial networks [16]}, [17]}, [18]}; some use recurrent neural networks [19]}, [20]}, [21]}; auto-encoders are also used [22]}, and recently weak supervision [23]} and digital twin-based transfer learning [24]} have been successfully implemented.
